Extremely Compact Integrate-and-Fire STT-MRAM Neuron: A Pathway toward All-Spin Artificial Deep Neural Network

Ming Hung Wu, Ming Chun Hong, Chih Cheng Chang, Paritosh Sahu, Jeng Hua Wei, Heng Yuan Lee, Shyh Shyuan Shcu, Tuo-Hung Hou

研究成果: Conference contribution同行評審

14 引文 斯高帕斯(Scopus)

摘要

This work reports the complete framework from device to architecture for deep learning acceleration in an all-spin artificial neural network (ANN) built by highly manufacturable STT-MRAM technology. The most compact analog integrate-and-fire neuron reported to date is developed based on the back-hopping oscillation in magnetic tunnel junctions. This novel device is unique because it performs numerous essential neural functions simultaneously, including current integration, voltage spike generation, state reset, and 4-bit precision. The device itself is also a stochastic binary synapse, and thus eases the implementation of the compact all-spin ANN with high accuracy for online training.

原文English
主出版物標題2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers
發行者Institute of Electrical and Electronics Engineers Inc.
頁面T34-T35
頁數2
ISBN(電子)9784863487178
DOIs
出版狀態Published - 6月 2019
事件39th Symposium on VLSI Technology, VLSI Technology 2019 - Kyoto, Japan
持續時間: 9 6月 201914 6月 2019

出版系列

名字Digest of Technical Papers - Symposium on VLSI Technology
2019-June
ISSN(列印)0743-1562

Conference

Conference39th Symposium on VLSI Technology, VLSI Technology 2019
國家/地區Japan
城市Kyoto
期間9/06/1914/06/19

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